Efficient Selection and Placement of In-Package Decoupling Capacitors Using Matrix-Based Evolutionary Computation

Jain, Akash; Vaghasiya, Heman; Tripathi, Jai Narayan

doi:10.1109/ojnano.2021.3133213

Cited by 10 publications

(4 citation statements)

References 40 publications

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“…This matrix-centric approach significantly enhances computational efficiency and strengthens the mathematical foundation of EC. At present, MEC has been successfully applied in various domains, such as integrated circuit interconnections [38], path planning [39], and function optimization [40]. However, research on MEC in feature selection is still absent, and how to make MEC solve discrete problems is still a challenge.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Matrix-Based Evolutionary Computation Framework for EEG Feature Selection

Duan,

Sun,

Yang

et al. 2024

Preprint

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Electroencephalogram (EEG) plays a significant role in emotion recognition because it contains abundant information. However, due to the highly correlated EEG channels, a lot of redundant EEG features exist, which not only potentially degrade the emotion recognition accuracy, but also bring high computational cost. To address this challenge, this paper proposes an adaptive matrix-based evolutionary computation (MEC) framework to select as a small number of informative EEG features as possible for effective emotion recognition. Unlike most existing EC algorithms that utilize vector-based operations, this framework leverages matrix-based operations to reduce feature redundancy and improve classification accuracy by dynamically adjusting the feature subset size according to the characteristics of the dataset. In such a way, the selection efficiency is largely improved. To verify the effectiveness and efficiency of this framework, the classical Genetic Algorithm (GA), the typical Particle Swarm Optimization (PSO) algorithm, and the classical Differential Evolution (DE) algorithm, are respectively embedded into this framework for EEG feature selection, and then evaluated on three widely used public EEG datasets for emotion recognition. Compared with several state-of-the-art EEG feature selection algorithms, the devised framework is much more effective in terms of the classification accuracy, the feature subset optimization, and the computational efficiency. In addition, the experimental results further reveal that the selected feature subsets are very different for different genders. This indicates the demand of gender-sensitive EEG feature selection for emotion recognition.

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Section: Related Workmentioning

confidence: 99%

An Adaptive Matrix-Based Evolutionary Computation Framework for EEG Feature Selection

Duan,

Sun,

Yang

et al. 2024

Preprint

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“…This study uses a practical system earlier used in [12], [16]. It's a usual practice to select capacitors based on the anti-resonance points in the PDN impedance profile and then to place them as close to the core circuit as feasible [8].…”

Section: Problem Descriptionmentioning

confidence: 99%

“…A proven time-effective optimization strategy for the decoupling capacitor optimization problem is to use the metaheuristic algorithms [11]. Matrix-based metaheuristic optimization has recently been used to speed up the process even further [12]. There are many other metaheuristic algorithms for optimization, but Particle Swarm Optimization (PSO) has emerged as one of the best and quickest methods to solve such problems [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Optimization of Decoupling Capacitors Using Adaptive Region Based Encoding Scheme in Particle Swarm Optimization

Junjariya

Tripathi

2022

IEEE Open J. Nanotechnol.

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Power delivery networks are responsible for supplying clean power to the integrated circuits. Power supply noise plays a critical role in determining the performance of high-speed very large scale integration circuits and systems. In order to maintain power integrity in high-speed systems, decoupling capacitors are used to maintain low impedance of the PDN to eventually minimize power supply noise. However, the discrete optimization problem of selecting decoupling capacitors becomes computationally challenging in the systems having stringent PI requirements. In this work, a novel approach using the Social-Learning Particle Swarm Optimization (SLPSO) technique along with Adaptive Region Search (ARS) is used to tackle the Large-Scale Optimization Problem (LSOP) of decoupling capacitor placement. Region Search (RS) is used to guide particles, followed by ARS to dynamical search for the local best positions and for particles to move faster across the search space while maintaining the diversity of the population. To demonstrate the proposed approach, three practical case studies are presented. The obtained results are compared with current state-of-the-art approaches. The proposed approach drastically reduces computation time and is consistent with better results than other approaches. This consistency of improvement in CPU time in the results of all the examples validates the proposed approach.

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“…In addition, the highly compact and large number of transistors in a circuit can cause more nonlinear effects by which the amplitude of supply noise harmonics rises, eventually leading to bit failure. Therefore, maintaining signal integrity (SI) and power integrity (PI) is very challenging in high-speed nanodevice-based circuits [7], [8], [9], [10]. The term SI and PI deals with the quality of signal and the quality of power, respectively.…”

Section: Introductionmentioning

confidence: 99%

Indefinite Admittance Matrix Based Modelling of PSIJ in Nano-Scale CMOS I/O Drivers

Sharma

Tripathi

Shrimali

2022

IEEE Open J. Nanotechnol.

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The past decade has witnessed a tremendous reduction in the feature size from the deepsubmicron to the advanced nano-scale CMOS devices. In nanoscale devices based high-speed systems, the budgeting of jitter due to supply fluctuations is one of the major performance bottlenecks while designing integrated circuits (ICs). In this paper, an accurate and efficient method to analyse power supply induced jitter (PSIJ) in CMOS N-stage inverters is developed using the estimation-by-inspection method. Based on the Indefinite Admittance Matrix, a reduced two-port network is developed for a multiple-input circuit, considering the presence of the supply/bulk/ground sources. The closed-form expressions of the PSIJ have been evaluated for a single and N-stages CMOS inverter chain. The expression is also valid for the PSIJ analysis at any intermediate stage of the N-stage chain. For validation purpose, the circuits are designed in a standard 28 nm CMOS technology with V DD of 1 V. The analytical results are compared with the simulation and the experiments. The maximum mean percentage error for EDA simulation and experimentally measured results are 2.4% and 13%, respectively. The proposed analysis is compared with some of the existing PSIJ modelling techniques and shows a significant improvement in speed-up factor and error percentage.INDEX TERMS Two-port network, indefinite admittance matrix (IAM), power supply induced jitter (PSIJ), nano-scale devices.

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Efficient Selection and Placement of In-Package Decoupling Capacitors Using Matrix-Based Evolutionary Computation

Cited by 10 publications

References 40 publications

An Adaptive Matrix-Based Evolutionary Computation Framework for EEG Feature Selection

An Adaptive Matrix-Based Evolutionary Computation Framework for EEG Feature Selection

Large-Scale Optimization of Decoupling Capacitors Using Adaptive Region Based Encoding Scheme in Particle Swarm Optimization

Indefinite Admittance Matrix Based Modelling of PSIJ in Nano-Scale CMOS I/O Drivers

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